The ability to locate and read the relevant information from a financial item is a valuable feature of information processing, and is especially useful in the processing of financial documents. Many financial documents, such as checks, contain entries made in a human-readable format such as printing or handwriting. Many of these entries are not made in a standard machine-readable format such as printing with magnetic ink according to a known standard such as E13B or the like. At least some of the non-standardized information appearing on a check must be translated to machine-readable format, or hand entered directly into a machine processing the check. For example, the amount of a check is typically not entered onto the check in machinereadable format at the time the check is written. The amount of the check, however, is critical to processing of the check, and must be communicated to the check-processing equipment. This has traditionally been done by human operators who read the amount written on the check and enter this amount into a machine which then prints the amount onto the check in magnetic ink.
More recently, however, it has become possible to devise techniques for machine-reading of the non-standardized information, in order to increase processing speed and reduce costs. This machine-reading is typically done by capturing and interpreting an image of the item in order to extract text fields. The captured image is typically a gray image, having areas of varying lightness and darkness; or in other words, pixels of differing gray scale.
Prior art methods typically begin by applying a binarization algorithm to the captured gray image of a document. This results in a binary image, where foreground pixels are black, and background pixels are white. Connected component analysis is performed on the binary image to assemble groups of touching black pixels. Connected components are then grouped into tokens, which are classified into horizontal lines, vertical lines, machine-printed text, and hand-printed text. Statistical features are extracted for each token. The document is classified based on the extracted tokens, where possible classifications include a business check, personal check, deposit slip, giro, or currency. Each area of machine-printed text and hand-printed text is grouped into a zone. Finally, optical character recognition is performed on the zones of interest.
However, it has become increasingly difficult to obtain a good quality binary image as financial institutions are using documents with more and more complex graphical and/or textured backgrounds embedded to prevent fraud. These backgrounds appear lighter on the documents than does the foreground information, but the binarization processes of the prior art remove the information contributed by the lightness of the background. When binarization is completed, the background material appears as dark as does the foreground material, making it difficult to extract the foreground material from the background material. Text recognition becomes more difficult and errors in extracting text from the binary image are more likely to occur.
There exists, therefore, a need in the art for a means for automatic extraction of information from a document which is less susceptible to interference by the presence of background material.